A developer recently built a fully serverless chatbot on AWS, combining Amazon Bedrock and Amazon Kendra. This hands-on workshop demonstrates how Retrieval-Augmented Generation (RAG) bridges the gap between powerful LLMs and private enterprise data, allowing models to generate accurate, context-aware responses without costly fine-tuning.

The architecture uses AWS Amplify for the frontend, API Gateway for routing, and AWS Lambda for compute. The flow is straightforward: a user query triggers a Lambda function that retrieves relevant documents from Kendra, augments the prompt with that context, and sends it to Bedrock’s foundation models for a grounded response.

This serverless approach offers significant cost efficiency and automatic scaling, making it ideal for internal enterprise assistants and compliance search tools. By abstracting LLM complexity and simplifying enterprise search, developers can rapidly deploy production-ready AI applications without managing underlying infrastructure.